The practice of medicine has greatly advanced with the improvement of medical imaging technology. Medical imaging may include many different types of image of the human body, including x-ray imaging, computed tomography (CT) scan imaging, magnetic resonance imaging (MRI), etc. Through the use of medical imaging technology, medical professionals are able to see images of internal organs, for example, of patients to help diagnose medical conditions of the patients. While medical imaging technology has significantly improved medical care, because of the nature of creating and interpreting medical images, radiological or other imaging technique, inaccuracies in the imaging and interpretation processes of the medical images result. Because of the inaccuracies in the imaging and interpretation processes, patient medical conditions are often misdiagnosed.
Misdiagnosis of a medical condition, such as a disease, may come in the form of false positives, false negatives, and false equivocal diagnoses. A false positive is, a detection of a disease that does not exist. A false negative is a failure to detect a disease that is present in a patient. A false equivocal diagnosis is a statement that a definitive diagnosis cannot be made based on the information available (e.g., “cancer cannot be ruled out”) when sufficient information is available to make a definitive diagnosis. Each of these misdiagnoses may result in higher costs of treatment, additional suffering to patients, lost productivity, exposure to treatments which themselves have side effects that diminish health status, and additional burden on the healthcare system as a whole. It has been estimated by the American College of Radiology that frequency of misdiagnosis of radiological imaging interpretations is as high as 30%. And, given that it has been estimated that 40% to 60% of total healthcare spend is influenced by radiological imaging and interpretations therefrom, misdiagnoses results from misinterpretation of medical imaging has a large impact on the healthcare system.
Medical literature documents significant variation in misdiagnosis rates among medical image reading professionals. Much of the misdiagnoses in reading medical images occurs from medical image reading professionals either not having enough experience or not having enough experience reading particular types of medical conditions. For example, a large scale study of proficiency in reading screening mammography studies concluded that false negative findings range from 3% to 71% (mean of 23%) and false positive findings range from 1% to 29% (mean of 10%), and that the higher overall accuracy was associated with more experience and a higher focus on screening mammograms. It is therefore reasonably predictable that a general radiologist who reads relatively few mammograms while attending to the broad range of clinical radiology needs of a rural community in which he or she resides, reading medical images for brain tumors, small cell lung cancer, or other diseases. Because of the broad nature of the clinical practice, the general radiologist is more likely to misdiagnose both mammograms and other medical conditions where experience is not sufficient to attain proficiency.